{
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   "cell_type": "markdown",
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   "source": [
    "# NEP tutorial\n",
    "## 1. Introduction\n",
    "\n",
    "- In this tutorial, we will show how to use the `nep` executable to train a NEP potential and check the results.\n",
    "\n",
    "- The NEP potential has been first introduced in **GPUMD-v2.6** and this version wil be called **NEP1**. It corresponds to the following paper: \n",
    "  - Zheyong Fan, Zezhu Zeng, Cunzhi Zhang, Yanzhou Wang, Keke Song, Haikuan Dong, Yue Chen, and Tapio Ala-Nissila, Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport, Phys. Rev. B **104**, 104309 (2021). https://doi.org/10.1103/PhysRevB.104.104309\n",
    "  \n",
    "- From **GPUMD-v2.7** to **GPUMD-v3.0** we have updated the NEP potential to **NEP2**, which corresponds to the following paper:\n",
    "  - Zheyong Fan, Improving the accuracy of the neuroevolution machine learning potential for multi-component systems, Journal of Physics: Condensed Matter **34** 125902 (2022). https://doi.org/10.1088/1361-648X/ac462b\n",
    "  \n",
    "- Now we are using **GPUMD-master**, which will be released as **GPUMD-v3.4**, in which we have updated the NEP potential to **NEP3**. which corresponds to the following paper:\n",
    "  - Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song, Junjie Wang, Yong Wang, Zezhu Zeng, Ke Xu, Eric Lindgren, J. Magnus Rahm, Alexander J. Gabourie, Jiahui Liu, Haikuan Dong, Jianyang Wu, Yue Chen, Zheng Zhong, Jian Sun, Paul Erhart, Yanjing Su, Tapio Ala-Nissila, GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations, The Journal of Chemical Physics. (2022) In press. https://doi.org/10.1063/5.0106617"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Prerequisites\n",
    "- You need to have access to an Nvidia GPU with compute capability >= 3.5. CUDA 9.0 or higher is also needed to compile the GPUMD package.\n",
    "- You can download **GPUMD-master**, unpack it, go to the `src` directory, and type `make -j` to compile. Then, you should get two executables: `gpumd` and `nep` in the `src` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train a NEP potential for PbTe\n",
    "- To train a NEP potential, one must prepare three input files: `train.xyz`, `test.xyz` and `nep.in`. The `train.xyz` file contains all the training data, the `test.xyz` file contains all the testing data, and the `nep.in` file contains some controlling parameters.\n",
    "\n",
    "### 3.1. Prepare the train.xyz and test.xyz files\n",
    "- The `train.xyz/test.xyz` file should be prepared according to the documentation: https://gpumd.zheyongfan.org/index.php/The_train.xyz_input_file\n",
    "- For the example in this tutorial, we have already prepared the `trian.xyz` and `test.xyz` files in the current folder, which contain 125 and 200 structures, respectively.\n",
    "- In this example, there are only energy and force training data. In general, one can also have virial training data.\n",
    "- We used the VASP code to calculate the training data, but `nep` does not care about this. You can generate the data in `train.xyz/test.xyz` using any method that works for you. For example, before doing new DFT calculations, you can first check if there are training data publicly available and then try to convert them into the format as required by `train.xyz/test.xyz`. There are quite a few tools (https://github.com/brucefan1983/GPUMD/tree/master/tools/nep_related) available for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Prepare the `nep.in` file. \n",
    "- First, study the document about this input file: https://gpumd.zheyongfan.org/index.php/The_nep.in_input_file\n",
    "- For the example in this tutorial, we have prepared the `nep.in` file in the current folder. \n",
    "- The `nep.in` file reads:\n",
    "```\n",
    "type         2 Te Pb\n",
    "version      3\n",
    "cutoff       8 4\n",
    "n_max        4 4\n",
    "l_max        4 2\n",
    "neuron       30\n",
    "batch        25\n",
    "generation   10000\n",
    "```\n",
    "- We explain it line by line below:\n",
    "  - There are $N_{\\rm typ}=2$ atoms types in the training set: Te and Pb. The user can also write Pb first and Te next. The code will remember the order of the atom types here and record it into the NEP potential file `nep.txt`. Therefore, the user does not need to remember the order of the atom types here.\n",
    "  - The NEP version is 3, which means NEP3.\n",
    "  - The radial and angular cutoff distances are $r_{\\rm c}^{\\rm R}=8$ angstrom and $r_{\\rm c}^{\\rm A}=4$ angstrom, respectively.\n",
    "  - The $n_{\\rm max}$ parameter for the radial and angular descriptor parts are $n_{\\rm max}^{\\rm R}=4$ and $n_{\\rm max}^{\\rm A}=4$, respectively.\n",
    "  - The $l_{\\rm max}$ parameter for the 3-body and 4-body descriptor parts are $l_{\\rm max}^{\\rm 3b}=4$ and $l_{\\rm max}^{\\rm 4b}=2$, respectively.\n",
    "  - The number of neurons in the hidden layer (there is only one hidden layer in NEP) is $N_{\\rm neu}=30$.\n",
    "  - The batch size is $N_{\\rm bat}=25$.\n",
    "  - The maximum number of generations is $N_{\\rm gen}=10^4$.\n",
    "  - All the other parameters will have default values (please check the screen output).\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Run the `nep` executable\n",
    " - Go to the current folder from command line and type\n",
    "```\n",
    "path/to/nep # linux\n",
    "path\\to\\nep # Windows\n",
    "```\n",
    " \n",
    " - It took about **11 min** to run 10,000 generations using my **laptop** with a GeForce RTX 2070 GPU card. \n",
    " - I have **restarted** the run once (**simply run the code again**) and this example effectively took about **22 min** in total."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Check the training results\n",
    "\n",
    "- After running the `nep` executable, there will be some output on the screen. We encourage the user to read the screen output carefully. It can help to understand the calculation flow of `nep`. \n",
    "- Some files will be generated in the current folder and will be updated every 100 generations (some will be updated every 1000 generations). Therefore, one can check the results even before finishing the maximum number of generations as set in `nep.in`. \n",
    "- If the user has not studied the documentation of the output files generated by `nep`, it is time to read it here: https://gpumd.zheyongfan.org/index.php/The_output_files_for_the_nep_executable\n",
    "- We will use Python to visualize the results in some of the output files next. We first load `pylab` that we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1. Checking the `loss.out` file. \n",
    "- We see that the $\\mathcal{L}_1$ and $\\mathcal{L}_2$ regularization loss functions first increases and then decreases, which indicates the effectiveness of the regularization.\n",
    "- The energy loss is the root mean square error (RMSE) of energy per atom, which converges to about 0.4 meV/atom. \n",
    "- The force loss is the RMSE of force components, which converges to about 40 meV/Angstrom. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = loadtxt('loss.out')\n",
    "loglog(loss[:, 1:6])\n",
    "loglog(loss[:, 7:9])\n",
    "xlabel('Generation/100')\n",
    "ylabel('Loss')\n",
    "legend(['Total', 'L1-regularization', 'L2-regularization', 'Energy-train', 'Force-train', 'Energy-test', 'Force-test'])\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Checking the `energy_test.out` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The dots are the raw data and the line represents the identity function used to guide the eyes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy_test = loadtxt('energy_test.out')\n",
    "plot(energy_test[:, 1], energy_test[:, 0], '.')\n",
    "plot(linspace(-3.85,-3.69), linspace(-3.85,-3.69), '-')\n",
    "xlabel('DFT energy (eV/atom)')\n",
    "ylabel('NEP energy (eV/atom)')\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Checking the `force_test.out` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The dots are the raw data and the line represents the identity function used to guide the eyes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "force_test = loadtxt('force_test.out')\n",
    "plot(force_test[:, 3:6], force_test[:, 0:3], '.')\n",
    "plot(linspace(-4,4), linspace(-4,4), '-')\n",
    "xlabel('DFT force (eV/A)')\n",
    "ylabel('NEP force (eV/A)')\n",
    "legend(['x direction', 'y direction', 'z direction'])\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Checking the `virial_test.out` file\n",
    "- In general, one can similarly check the `virial.out` file, but in this particular example, virial data were not used in the training process (no virial data exist in `train.xyz`). \n",
    "- However, `virial.out` still contains the predicted virial data for each structure in the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "virial_test = loadtxt('virial_test.out')\n",
    "plot(virial_test[:, 1], virial_test[:, 0], '.')\n",
    "plot(linspace(-2,2), linspace(-2,2), '-')\n",
    "xlabel('DFT virial (eV/atom)')\n",
    "ylabel('NEP virial (eV/atom)')\n",
    "tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5. Checking the other files\n",
    "- One can similarly check the other files: `energy_train.out`, `force_train.out`, and `virial_train.out`. "
   ]
  },
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    "## 5. Restart\n",
    "- After each 100 steps, the `nep.restart` file will be updated.\n",
    "- If `nep.restart` exists, it means you want to continue the previous training. Remember not to change the parameters related to the descriptor and the number of neurons. Otherwise the restarting behavior is undefined.\n",
    "- If you want to train from scratch, you need to delete `nep.restart` first (better to first make a copy of all the results from the previous training)."
   ]
  }
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